Nonequilibrium model of short-range repression in gene transcription regulation

TL;DR

This paper introduces a nonequilibrium model for short-range repression in gene transcription, revealing two distinct repression mechanisms and outperforming equilibrium models in explaining experimental data.

Contribution

It develops a general nonequilibrium framework for modeling short-range repression, distinguishing two mechanisms and improving data fit over traditional equilibrium models.

Findings

Better fit to Drosophila gene expression data

Identifies two distinct repression mechanisms

Predicts the dominance of chromatin recruitment in repression

Abstract

Transcription factors are proteins that regulate gene activity by activating or repressing gene transcription. A special class of transcriptional repressors operates via a short-range mechanism, making local DNA regions inaccessible to binding by activators, and thus providing an indirect repressive action on the target gene. This mechanism is commonly modeled assuming that repressors interact with DNA under thermodynamic equilibrium and neglecting some configurations of the gene regulatory region. We elaborate on a more general nonequilibrium model of short-range repression using the graph formalism for transitions between gene states, and we apply analytical calculations to compare it with the equilibrium model in terms of the repression strength and expression noise. In contrast to the equilibrium approach, the new model allows us to separate two basic mechanisms of short-range repression. The first mechanism is associated with the recruiting of factors that mediate chromatin condensation, and the second one concerns the blocking of factors that mediate chromatin loosening. The nonequilibrium model demonstrates better performance on previously published gene expression data obtained for transcription factors controlling Drosophila development, and furthermore it predicts that the first repression mechanism is the most favorable in this system. The presented approach can be scaled to larger gene networks and can be used to infer specific modes and parameters of transcriptional regulation from gene expression data.